In the conventional network environments, the Full-Duplex Radio (FDR) architecture allows for data transmissions at a same timing and at a same frequency as compared to Time-Division Duplex (TDD) and the Frequency-Division Duplex (FDD), so the FDR architecture has significantly higher transmission efficiency.
Because a single device transmits and receives data at a same timing and at a same frequency under the FDR network architecture, signals transmitted by the single device itself also causes signal interference to the device itself. To solve such a self-interference problem, linear digital self-interference estimation methods have been developed.
However, most of the linear digital self-interference estimation methods currently used adopt the Maximum Likelihood estimation algorithm, the main idea of which is to estimate an optimal solution of the self-interference signal through multiple rounds of operations and perform signal interference cancellation accordingly.
Unfortunately, such methods leads to a high operational complexity, and because they are only able to calculate the optimal solution of the self-interference signal roughly according to the overall network environment, it is impossible to determine a self-interference signal that better complies with the usage condition of the device by taking the signal memory problem possibly caused by the amplifier of the radio frequency (RF) circuit of the device into consideration.
Accordingly, efforts have to be made in the art to make an improvement on the shortcoming of self-interference signal estimation under the FDR architecture.